Disjointed, incompatible, or inaccessible data is unusable. This causes inefficiencies, and limits the use of business intelligence (BI) to accomplish successful business strategies. When data can’t be used for actionable insights, businesses overlook opportunities and become less able to compete.
What are Data Silos?
Data silos – where data is trapped and isolated – are a widespread problem. A recent survey found that 83% of executives think that their companies have silos, and 97% believe that it is having a negative effect on business.
Data can be siloed for many reasons. For example, when data is drawn from different sources, it is often of distinct formats that are incompatible with one another. This causes data to be siloed. Data silos can also occur when data that is stored in different environments – say, on-premises versus the cloud – could be difficult to integrate, and may be inaccessible to various users.
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What is a Data Fabric?
Data fabric, simply put, is the answer to this problem. A data fabric is an environment that standardizes and unifies data from different sources, storage locations, and access points to make it usable, scalable, and integrated. It creates a unified framework that makes data seamless by design.
Data fabric – the standardization and unification of data across an entire organization – was named one of the top 10 data trends by Gartner; who predicted that businesses would be forced to invest in creating data fabrics to improve business intelligence.
How Data Fabrics Can Solve Data Silo Problems
Breaking down data silos can provide a variety of benefits to enterprises. When implemented properly, data fabrics can solve challenges associated with data silos. These challenges include availability, scalability, and reliability.
With a well-structured data fabric, information is made available to users regardless of the source of the data, the location where it is stored, or the point of user access. Siloed data is often availability-restricted, whereas accessible data is available without structural restrictions
Data that is automatically standardized upon receipt can be processed at higher volumes than data that must be manually formatted, standardized and integrated. The greater the volume of data that can be applied, the better the business insights drawn from that data.
A data fabric improves data reliability in two different ways. First, data is more reliably available to a variety of users regardless of geographic distribution. Secondly, data is unified to a single source. This eliminates confusion which could come from having data stored on multiple platforms. By using a data fabric, businesses can make faster decisions because they can view data from multiple sources in one platform.
One of the main reasons to create a data fabric is to improve the usability of different types of data. A multi-faceted, multi-layered data analytics program is the best way to achieve valuable data insights – and stay one step ahead of the competition. A comprehensive data analytics program should include different types of data, each providing another level of insight to the business environment.
Location intelligence is one type of business intelligence that can add unprecedented insight to a data analytics program. With location intelligence, a company can understand not just who a customer is, but also what they do, in the real world – their habits, their interests, and their values.
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